Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations9994
Missing cells543
Missing cells (%)0.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory6.8 MiB
Average record size in memory718.1 B

Variable types

Numeric7
Categorical10
Text3
DateTime1

Alerts

Country has constant value "United States" Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Category is highly overall correlated with Sub-CategoryHigh correlation
Days to Ship Actual is highly overall correlated with Days to Ship Scheduled and 2 other fieldsHigh correlation
Days to Ship Scheduled is highly overall correlated with Days to Ship Actual and 2 other fieldsHigh correlation
Discount is highly overall correlated with Profit Ratio and 1 other fieldsHigh correlation
Profit Ratio is highly overall correlated with DiscountHigh correlation
Profit per Order is highly overall correlated with Discount and 1 other fieldsHigh correlation
Region is highly overall correlated with StateHigh correlation
Sales per Customer is highly overall correlated with Profit per OrderHigh correlation
Ship Mode is highly overall correlated with Days to Ship Actual and 1 other fieldsHigh correlation
Ship Status is highly overall correlated with Days to Ship Actual and 1 other fieldsHigh correlation
State is highly overall correlated with RegionHigh correlation
Sub-Category is highly overall correlated with CategoryHigh correlation
Ship Mode has 543 (5.4%) missing values Missing
Days to Ship Actual has 519 (5.2%) zeros Zeros
Discount has 4798 (48.0%) zeros Zeros

Reproduction

Analysis started2025-07-02 21:55:14.329661
Analysis finished2025-07-02 21:55:24.497867
Duration10.17 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Days to Ship Actual
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9581749
Minimum0
Maximum7
Zeros519
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-02T21:55:24.578930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7475667
Coefficient of variation (CV)0.44150822
Kurtosis-0.28755198
Mean3.9581749
Median Absolute Deviation (MAD)1
Skewness-0.42132235
Sum39558
Variance3.0539895
MonotonicityNot monotonic
2025-07-02T21:55:24.660323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 2774
27.8%
5 2169
21.7%
2 1334
13.3%
6 1203
12.0%
3 1005
 
10.1%
7 621
 
6.2%
0 519
 
5.2%
1 369
 
3.7%
ValueCountFrequency (%)
0 519
 
5.2%
1 369
 
3.7%
2 1334
13.3%
3 1005
 
10.1%
4 2774
27.8%
5 2169
21.7%
6 1203
12.0%
7 621
 
6.2%
ValueCountFrequency (%)
7 621
 
6.2%
6 1203
12.0%
5 2169
21.7%
4 2774
27.8%
3 1005
 
10.1%
2 1334
13.3%
1 369
 
3.7%
0 519
 
5.2%

Ship Status
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size568.8 KiB
2
4909 
-1
2639 
1
2446 

Length

Max length2
Median length1
Mean length1.2640584
Min length1

Characters and Unicode

Total characters12633
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row-1
4th row-1
5th row-1

Common Values

ValueCountFrequency (%)
2 4909
49.1%
-1 2639
26.4%
1 2446
24.5%

Length

2025-07-02T21:55:24.772237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:24.874449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5085
50.9%
2 4909
49.1%

Most occurring characters

ValueCountFrequency (%)
1 5085
40.3%
2 4909
38.9%
- 2639
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9994
79.1%
Dash Punctuation 2639
 
20.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5085
50.9%
2 4909
49.1%
Dash Punctuation
ValueCountFrequency (%)
- 2639
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12633
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5085
40.3%
2 4909
38.9%
- 2639
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5085
40.3%
2 4909
38.9%
- 2639
20.9%

Days to Ship Scheduled
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size566.2 KiB
6
5968 
3
1945 
1
1538 
0
 
543

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9994
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Length

2025-07-02T21:55:24.961828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:25.033693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Most occurring characters

ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9994
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9994
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9994
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 5968
59.7%
3 1945
 
19.5%
1 1538
 
15.4%
0 543
 
5.4%

Sales per Customer
Real number (ℝ)

High correlation 

Distinct5456
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.85799
Minimum0.44
Maximum22638.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-02T21:55:25.149807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile4.98
Q117.28
median54.49
Q3209.94
95-th percentile956.982
Maximum22638.48
Range22638.04
Interquartile range (IQR)192.66

Descriptive statistics

Standard deviation623.24512
Coefficient of variation (CV)2.7114355
Kurtosis305.31173
Mean229.85799
Median Absolute Deviation (MAD)45.405
Skewness12.972752
Sum2297200.8
Variance388434.48
MonotonicityNot monotonic
2025-07-02T21:55:25.295036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.96 57
 
0.6%
19.44 39
 
0.4%
15.55 39
 
0.4%
25.92 36
 
0.4%
10.37 36
 
0.4%
32.4 28
 
0.3%
17.94 21
 
0.2%
6.48 21
 
0.2%
20.74 19
 
0.2%
14.94 17
 
0.2%
Other values (5446) 9681
96.9%
ValueCountFrequency (%)
0.44 1
 
< 0.1%
0.56 1
 
< 0.1%
0.84 1
 
< 0.1%
0.85 1
 
< 0.1%
0.88 1
 
< 0.1%
0.9 1
 
< 0.1%
0.98 1
 
< 0.1%
0.99 1
 
< 0.1%
1.04 1
 
< 0.1%
1.08 3
< 0.1%
ValueCountFrequency (%)
22638.48 1
< 0.1%
17499.95 1
< 0.1%
13999.96 1
< 0.1%
11199.97 1
< 0.1%
10499.97 1
< 0.1%
9892.74 1
< 0.1%
9449.95 1
< 0.1%
9099.93 1
< 0.1%
8749.95 1
< 0.1%
8399.98 1
< 0.1%

Profit Ratio
Real number (ℝ)

High correlation 

Distinct288
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.037092
Minimum-275
Maximum50
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2025-07-02T21:55:25.432739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-275
5-th percentile-76.7
Q17.5
median27
Q336.3
95-th percentile48
Maximum50
Range325
Interquartile range (IQR)28.8

Descriptive statistics

Standard deviation46.677878
Coefficient of variation (CV)3.8778367
Kurtosis10.172067
Mean12.037092
Median Absolute Deviation (MAD)17
Skewness-2.8947013
Sum120298.7
Variance2178.8243
MonotonicityNot monotonic
2025-07-02T21:55:25.589553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 509
 
5.1%
35 443
 
4.4%
47 353
 
3.5%
26 325
 
3.3%
49 315
 
3.2%
33.8 286
 
2.9%
29 278
 
2.8%
46 267
 
2.7%
28 260
 
2.6%
36.3 252
 
2.5%
Other values (278) 6706
67.1%
ValueCountFrequency (%)
-275 4
 
< 0.1%
-270 14
0.1%
-265 5
 
0.1%
-260 10
0.1%
-255 13
0.1%
-250 10
0.1%
-245 1
 
< 0.1%
-235 2
 
< 0.1%
-230 1
 
< 0.1%
-225 2
 
< 0.1%
ValueCountFrequency (%)
50 140
 
1.4%
49 315
3.2%
48 509
5.1%
47 353
3.5%
46 267
2.7%
45 220
2.2%
44 77
 
0.8%
43.3 1
 
< 0.1%
43 59
 
0.6%
42 69
 
0.7%

Category
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size681.4 KiB
Office Supplies
6026 
Furniture
2121 
Technology
1847 

Length

Max length15
Median length15
Mean length12.802582
Min length9

Characters and Unicode

Total characters127949
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowOffice Supplies
4th rowFurniture
5th rowOffice Supplies

Common Values

ValueCountFrequency (%)
Office Supplies 6026
60.3%
Furniture 2121
 
21.2%
Technology 1847
 
18.5%

Length

2025-07-02T21:55:25.715900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:25.790913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
office 6026
37.6%
supplies 6026
37.6%
furniture 2121
 
13.2%
technology 1847
 
11.5%

Most occurring characters

ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
S 6026
 
4.7%
6026
 
4.7%
Other values (10) 29560
23.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105903
82.8%
Uppercase Letter 16020
 
12.5%
Space Separator 6026
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16020
15.1%
i 14173
13.4%
p 12052
11.4%
f 12052
11.4%
u 10268
9.7%
c 7873
7.4%
l 7873
7.4%
s 6026
 
5.7%
r 4242
 
4.0%
n 3968
 
3.7%
Other values (5) 11356
10.7%
Uppercase Letter
ValueCountFrequency (%)
O 6026
37.6%
S 6026
37.6%
F 2121
 
13.2%
T 1847
 
11.5%
Space Separator
ValueCountFrequency (%)
6026
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 121923
95.3%
Common 6026
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16020
13.1%
i 14173
11.6%
p 12052
9.9%
f 12052
9.9%
u 10268
8.4%
c 7873
 
6.5%
l 7873
 
6.5%
O 6026
 
4.9%
S 6026
 
4.9%
s 6026
 
4.9%
Other values (9) 23534
19.3%
Common
ValueCountFrequency (%)
6026
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16020
12.5%
i 14173
11.1%
p 12052
9.4%
f 12052
9.4%
u 10268
 
8.0%
c 7873
 
6.2%
l 7873
 
6.2%
O 6026
 
4.7%
S 6026
 
4.7%
6026
 
4.7%
Other values (10) 29560
23.1%

City
Text

Distinct531
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size647.5 KiB
2025-07-02T21:55:26.104495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length9.3306984
Min length4

Characters and Unicode

Total characters93251
Distinct characters51
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)0.7%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowLos Angeles
4th rowFort Lauderdale
5th rowFort Lauderdale
ValueCountFrequency (%)
city 994
 
7.0%
new 937
 
6.6%
york 920
 
6.5%
san 805
 
5.7%
los 747
 
5.2%
angeles 747
 
5.2%
philadelphia 537
 
3.8%
francisco 510
 
3.6%
seattle 428
 
3.0%
houston 377
 
2.6%
Other values (555) 7234
50.8%
2025-07-02T21:55:26.587807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74773
80.2%
Uppercase Letter 14236
 
15.3%
Space Separator 4242
 
4.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8719
11.7%
a 7591
10.2%
o 7499
10.0%
i 6229
 
8.3%
n 6199
 
8.3%
l 5986
 
8.0%
s 4699
 
6.3%
r 4468
 
6.0%
t 4438
 
5.9%
c 2393
 
3.2%
Other values (16) 16552
22.1%
Uppercase Letter
ValueCountFrequency (%)
C 2085
14.6%
S 1740
12.2%
L 1295
9.1%
A 1242
8.7%
N 1134
8.0%
P 1013
 
7.1%
Y 940
 
6.6%
F 794
 
5.6%
D 627
 
4.4%
H 617
 
4.3%
Other values (14) 2749
19.3%
Space Separator
ValueCountFrequency (%)
4242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89009
95.5%
Common 4242
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8719
 
9.8%
a 7591
 
8.5%
o 7499
 
8.4%
i 6229
 
7.0%
n 6199
 
7.0%
l 5986
 
6.7%
s 4699
 
5.3%
r 4468
 
5.0%
t 4438
 
5.0%
c 2393
 
2.7%
Other values (40) 30788
34.6%
Common
ValueCountFrequency (%)
4242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8719
 
9.4%
a 7591
 
8.1%
o 7499
 
8.0%
i 6229
 
6.7%
n 6199
 
6.6%
l 5986
 
6.4%
s 4699
 
5.0%
r 4468
 
4.8%
t 4438
 
4.8%
4242
 
4.5%
Other values (41) 33181
35.6%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size683.3 KiB
United States
9994 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters129922
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 9994
100.0%

Length

2025-07-02T21:55:26.715422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:26.777468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united 9994
50.0%
states 9994
50.0%

Most occurring characters

ValueCountFrequency (%)
t 29982
23.1%
e 19988
15.4%
n 9994
 
7.7%
U 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 99940
76.9%
Uppercase Letter 19988
 
15.4%
Space Separator 9994
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29982
30.0%
e 19988
20.0%
n 9994
 
10.0%
i 9994
 
10.0%
d 9994
 
10.0%
a 9994
 
10.0%
s 9994
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
U 9994
50.0%
S 9994
50.0%
Space Separator
ValueCountFrequency (%)
9994
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119928
92.3%
Common 9994
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29982
25.0%
e 19988
16.7%
n 9994
 
8.3%
U 9994
 
8.3%
i 9994
 
8.3%
d 9994
 
8.3%
S 9994
 
8.3%
a 9994
 
8.3%
s 9994
 
8.3%
Common
ValueCountFrequency (%)
9994
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129922
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 29982
23.1%
e 19988
15.4%
n 9994
 
7.7%
U 9994
 
7.7%
i 9994
 
7.7%
d 9994
 
7.7%
9994
 
7.7%
S 9994
 
7.7%
a 9994
 
7.7%
s 9994
 
7.7%
Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size686.4 KiB
2025-07-02T21:55:27.061697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.960676
Min length7

Characters and Unicode

Total characters129529
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowDarrin Van Huff
4th rowSean O'Donnell
5th rowSean O'Donnell
ValueCountFrequency (%)
michael 120
 
0.6%
frank 112
 
0.6%
john 107
 
0.5%
patrick 96
 
0.5%
brian 93
 
0.5%
stewart 93
 
0.5%
paul 92
 
0.5%
rick 91
 
0.5%
ken 91
 
0.5%
matt 86
 
0.4%
Other values (901) 19072
95.1%
2025-07-02T21:55:27.534716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (47) 45608
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98856
76.3%
Uppercase Letter 20461
 
15.8%
Space Separator 10059
 
7.8%
Other Punctuation 124
 
0.1%
Dash Punctuation 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12011
12.1%
e 11836
12.0%
n 10241
10.4%
r 9530
9.6%
i 7919
 
8.0%
l 6494
 
6.6%
o 5850
 
5.9%
t 5435
 
5.5%
s 4546
 
4.6%
h 3857
 
3.9%
Other values (18) 21137
21.4%
Uppercase Letter
ValueCountFrequency (%)
C 1830
 
8.9%
S 1798
 
8.8%
M 1749
 
8.5%
B 1696
 
8.3%
D 1325
 
6.5%
A 1282
 
6.3%
J 1134
 
5.5%
P 1105
 
5.4%
H 1005
 
4.9%
K 964
 
4.7%
Other values (16) 6573
32.1%
Space Separator
ValueCountFrequency (%)
10059
100.0%
Other Punctuation
ValueCountFrequency (%)
' 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 119317
92.1%
Common 10212
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12011
 
10.1%
e 11836
 
9.9%
n 10241
 
8.6%
r 9530
 
8.0%
i 7919
 
6.6%
l 6494
 
5.4%
o 5850
 
4.9%
t 5435
 
4.6%
s 4546
 
3.8%
h 3857
 
3.2%
Other values (44) 41598
34.9%
Common
ValueCountFrequency (%)
10059
98.5%
' 124
 
1.2%
- 29
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 129440
99.9%
None 89
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 12011
 
9.3%
e 11836
 
9.1%
n 10241
 
7.9%
10059
 
7.8%
r 9530
 
7.4%
i 7919
 
6.1%
l 6494
 
5.0%
o 5850
 
4.5%
t 5435
 
4.2%
s 4546
 
3.5%
Other values (44) 45519
35.2%
None
ValueCountFrequency (%)
ö 61
68.5%
ä 23
 
25.8%
ü 5
 
5.6%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.620272
Minimum0
Maximum80
Zeros4798
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-02T21:55:27.647708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q320
95-th percentile70
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation20.645197
Coefficient of variation (CV)1.3216925
Kurtosis2.4095461
Mean15.620272
Median Absolute Deviation (MAD)20
Skewness1.6842947
Sum156109
Variance426.22415
MonotonicityNot monotonic
2025-07-02T21:55:27.749412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 4798
48.0%
20 3657
36.6%
70 418
 
4.2%
80 300
 
3.0%
30 227
 
2.3%
40 206
 
2.1%
60 138
 
1.4%
10 94
 
0.9%
50 66
 
0.7%
15 52
 
0.5%
Other values (2) 38
 
0.4%
ValueCountFrequency (%)
0 4798
48.0%
10 94
 
0.9%
15 52
 
0.5%
20 3657
36.6%
30 227
 
2.3%
32 27
 
0.3%
40 206
 
2.1%
45 11
 
0.1%
50 66
 
0.7%
60 138
 
1.4%
ValueCountFrequency (%)
80 300
 
3.0%
70 418
 
4.2%
60 138
 
1.4%
50 66
 
0.7%
45 11
 
0.1%
40 206
 
2.1%
32 27
 
0.3%
30 227
 
2.3%
20 3657
36.6%
15 52
 
0.5%
Distinct1237
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
Minimum2014-01-03 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-02T21:55:27.875534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:28.022822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1850
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Memory size939.5 KiB
2025-07-02T21:55:28.395931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length127
Median length78
Mean length36.914449
Min length5

Characters and Unicode

Total characters368923
Distinct characters85
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)0.9%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowSelf-Adhesive Address Labels for Typewriters by Universal
4th rowBretford CR4500 Series Slim Rectangular Table
5th rowEldon Fold 'N Roll Cart System
ValueCountFrequency (%)
xerox 865
 
1.5%
x 701
 
1.3%
with 599
 
1.1%
599
 
1.1%
avery 557
 
1.0%
for 539
 
1.0%
binders 524
 
0.9%
chair 479
 
0.9%
black 426
 
0.8%
phone 374
 
0.7%
Other values (2798) 50371
89.9%
2025-07-02T21:55:28.925398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
45654
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (75) 150106
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 238253
64.6%
Uppercase Letter 56270
 
15.3%
Space Separator 46081
 
12.5%
Decimal Number 17981
 
4.9%
Other Punctuation 7152
 
1.9%
Dash Punctuation 2940
 
0.8%
Final Punctuation 67
 
< 0.1%
Close Punctuation 60
 
< 0.1%
Open Punctuation 60
 
< 0.1%
Math Symbol 35
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33538
14.1%
r 20791
 
8.7%
o 19902
 
8.4%
a 19064
 
8.0%
i 18648
 
7.8%
l 16365
 
6.9%
n 15622
 
6.6%
s 14683
 
6.2%
t 14550
 
6.1%
c 8924
 
3.7%
Other values (18) 56166
23.6%
Uppercase Letter
ValueCountFrequency (%)
S 6281
 
11.2%
C 6007
 
10.7%
B 5530
 
9.8%
P 4918
 
8.7%
A 2948
 
5.2%
D 2941
 
5.2%
M 2870
 
5.1%
T 2616
 
4.6%
F 2510
 
4.5%
L 2284
 
4.1%
Other values (16) 17365
30.9%
Other Punctuation
ValueCountFrequency (%)
, 3120
43.6%
/ 1561
21.8%
" 1300
18.2%
. 463
 
6.5%
& 287
 
4.0%
' 257
 
3.6%
# 90
 
1.3%
% 45
 
0.6%
! 9
 
0.1%
* 9
 
0.1%
Other values (2) 11
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 3783
21.0%
0 2921
16.2%
2 2270
12.6%
4 1725
9.6%
3 1530
8.5%
5 1443
 
8.0%
8 1254
 
7.0%
9 1234
 
6.9%
6 941
 
5.2%
7 880
 
4.9%
Space Separator
ValueCountFrequency (%)
45654
99.1%
  427
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 2940
100.0%
Final Punctuation
ValueCountFrequency (%)
67
100.0%
Close Punctuation
ValueCountFrequency (%)
) 60
100.0%
Open Punctuation
ValueCountFrequency (%)
( 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 35
100.0%
Initial Punctuation
ValueCountFrequency (%)
19
100.0%
Other Number
ValueCountFrequency (%)
¾ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 294523
79.8%
Common 74400
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33538
 
11.4%
r 20791
 
7.1%
o 19902
 
6.8%
a 19064
 
6.5%
i 18648
 
6.3%
l 16365
 
5.6%
n 15622
 
5.3%
s 14683
 
5.0%
t 14550
 
4.9%
c 8924
 
3.0%
Other values (44) 112436
38.2%
Common
ValueCountFrequency (%)
45654
61.4%
1 3783
 
5.1%
, 3120
 
4.2%
- 2940
 
4.0%
0 2921
 
3.9%
2 2270
 
3.1%
4 1725
 
2.3%
/ 1561
 
2.1%
3 1530
 
2.1%
5 1443
 
1.9%
Other values (21) 7453
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 368388
99.9%
None 449
 
0.1%
Punctuation 86
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
45654
 
12.4%
e 33538
 
9.1%
r 20791
 
5.6%
o 19902
 
5.4%
a 19064
 
5.2%
i 18648
 
5.1%
l 16365
 
4.4%
n 15622
 
4.2%
s 14683
 
4.0%
t 14550
 
3.9%
Other values (69) 149571
40.6%
None
ValueCountFrequency (%)
  427
95.1%
é 14
 
3.1%
¾ 5
 
1.1%
à 3
 
0.7%
Punctuation
ValueCountFrequency (%)
67
77.9%
19
 
22.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7895737
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-02T21:55:29.027266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2251097
Coefficient of variation (CV)0.58716622
Kurtosis1.9918894
Mean3.7895737
Median Absolute Deviation (MAD)1
Skewness1.2785448
Sum37873
Variance4.9511131
MonotonicityNot monotonic
2025-07-02T21:55:29.122029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3 2409
24.1%
2 2402
24.0%
5 1230
12.3%
4 1191
11.9%
1 899
 
9.0%
7 606
 
6.1%
6 572
 
5.7%
9 258
 
2.6%
8 257
 
2.6%
10 57
 
0.6%
Other values (4) 113
 
1.1%
ValueCountFrequency (%)
1 899
 
9.0%
2 2402
24.0%
3 2409
24.1%
4 1191
11.9%
5 1230
12.3%
6 572
 
5.7%
7 606
 
6.1%
8 257
 
2.6%
9 258
 
2.6%
10 57
 
0.6%
ValueCountFrequency (%)
14 29
 
0.3%
13 27
 
0.3%
12 23
 
0.2%
11 34
 
0.3%
10 57
 
0.6%
9 258
 
2.6%
8 257
 
2.6%
7 606
6.1%
6 572
5.7%
5 1230
12.3%

Region
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size603.9 KiB
West
3203 
East
2848 
Central
2323 
South
1620 

Length

Max length7
Median length4
Mean length4.8594156
Min length4

Characters and Unicode

Total characters48565
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowWest
4th rowSouth
5th rowSouth

Common Values

ValueCountFrequency (%)
West 3203
32.0%
East 2848
28.5%
Central 2323
23.2%
South 1620
16.2%

Length

2025-07-02T21:55:29.237520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:29.325038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west 3203
32.0%
east 2848
28.5%
central 2323
23.2%
south 1620
16.2%

Most occurring characters

ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38571
79.4%
Uppercase Letter 9994
 
20.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 9994
25.9%
s 6051
15.7%
e 5526
14.3%
a 5171
13.4%
n 2323
 
6.0%
r 2323
 
6.0%
l 2323
 
6.0%
o 1620
 
4.2%
u 1620
 
4.2%
h 1620
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
W 3203
32.0%
E 2848
28.5%
C 2323
23.2%
S 1620
16.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48565
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48565
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 9994
20.6%
s 6051
12.5%
e 5526
11.4%
a 5171
10.6%
W 3203
 
6.6%
E 2848
 
5.9%
C 2323
 
4.8%
n 2323
 
4.8%
r 2323
 
4.8%
l 2323
 
4.8%
Other values (4) 6480
13.3%

Profit per Order
Real number (ℝ)

High correlation 

Distinct5157
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656785
Minimum-6599.98
Maximum8399.98
Zeros65
Zeros (%)0.7%
Negative1871
Negative (%)18.7%
Memory size78.2 KiB
2025-07-02T21:55:29.763702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6599.98
5-th percentile-53.031
Q11.73
median8.665
Q329.36
95-th percentile168.47
Maximum8399.98
Range14999.96
Interquartile range (IQR)27.63

Descriptive statistics

Standard deviation234.26014
Coefficient of variation (CV)8.1746834
Kurtosis397.18871
Mean28.656785
Median Absolute Deviation (MAD)10.78
Skewness7.5614403
Sum286395.91
Variance54877.815
MonotonicityNot monotonic
2025-07-02T21:55:29.908317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
 
0.7%
6.22 50
 
0.5%
9.33 39
 
0.4%
3.63 35
 
0.4%
5.44 33
 
0.3%
15.55 26
 
0.3%
7.26 21
 
0.2%
12.44 21
 
0.2%
3.11 20
 
0.2%
6.87 16
 
0.2%
Other values (5147) 9668
96.7%
ValueCountFrequency (%)
-6599.98 1
< 0.1%
-3839.99 1
< 0.1%
-3701.89 1
< 0.1%
-3399.98 1
< 0.1%
-2929.48 1
< 0.1%
-2639.99 1
< 0.1%
-2287.78 1
< 0.1%
-1862.31 1
< 0.1%
-1850.95 1
< 0.1%
-1811.08 1
< 0.1%
ValueCountFrequency (%)
8399.98 1
< 0.1%
6719.98 1
< 0.1%
5039.99 1
< 0.1%
4946.37 1
< 0.1%
4630.48 1
< 0.1%
3919.99 1
< 0.1%
3177.47 1
< 0.1%
2799.98 1
< 0.1%
2591.96 1
< 0.1%
2504.22 1
< 0.1%

Segment
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size642.7 KiB
Consumer
5191 
Corporate
3020 
Home Office
1783 

Length

Max length11
Median length8
Mean length8.8374024
Min length8

Characters and Unicode

Total characters88321
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowCorporate
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer 5191
51.9%
Corporate 3020
30.2%
Home Office 1783
 
17.8%

Length

2025-07-02T21:55:30.030673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:30.103189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer 5191
44.1%
corporate 3020
25.6%
home 1783
 
15.1%
office 1783
 
15.1%

Most occurring characters

ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
u 5191
 
5.9%
s 5191
 
5.9%
n 5191
 
5.9%
f 3566
 
4.0%
p 3020
 
3.4%
Other values (7) 14955
16.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74761
84.6%
Uppercase Letter 11777
 
13.3%
Space Separator 1783
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 13014
17.4%
e 11777
15.8%
r 11231
15.0%
m 6974
9.3%
u 5191
 
6.9%
s 5191
 
6.9%
n 5191
 
6.9%
f 3566
 
4.8%
p 3020
 
4.0%
a 3020
 
4.0%
Other values (3) 6586
8.8%
Uppercase Letter
ValueCountFrequency (%)
C 8211
69.7%
H 1783
 
15.1%
O 1783
 
15.1%
Space Separator
ValueCountFrequency (%)
1783
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 86538
98.0%
Common 1783
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 13014
15.0%
e 11777
13.6%
r 11231
13.0%
C 8211
9.5%
m 6974
8.1%
u 5191
 
6.0%
s 5191
 
6.0%
n 5191
 
6.0%
f 3566
 
4.1%
p 3020
 
3.5%
Other values (6) 13172
15.2%
Common
ValueCountFrequency (%)
1783
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88321
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 13014
14.7%
e 11777
13.3%
r 11231
12.7%
C 8211
9.3%
m 6974
7.9%
u 5191
 
5.9%
s 5191
 
5.9%
n 5191
 
5.9%
f 3566
 
4.0%
p 3020
 
3.4%
Other values (7) 14955
16.9%

Ship Mode
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing543
Missing (%)5.4%
Memory size587.8 KiB
0.0
5968 
1.0
1945 
2.0
1538 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters28353
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5968
59.7%
1.0 1945
 
19.5%
2.0 1538
 
15.4%
(Missing) 543
 
5.4%

Length

2025-07-02T21:55:30.190110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:30.253200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5968
63.1%
1.0 1945
 
20.6%
2.0 1538
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 15419
54.4%
. 9451
33.3%
1 1945
 
6.9%
2 1538
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18902
66.7%
Other Punctuation 9451
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15419
81.6%
1 1945
 
10.3%
2 1538
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 9451
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28353
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15419
54.4%
. 9451
33.3%
1 1945
 
6.9%
2 1538
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15419
54.4%
. 9451
33.3%
1 1945
 
6.9%
2 1538
 
5.4%

State
Categorical

High correlation 

Distinct49
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size639.3 KiB
California
2001 
New York
1128 
Texas
985 
Pennsylvania
587 
Washington
506 
Other values (44)
4787 

Length

Max length20
Median length14
Mean length8.4871923
Min length4

Characters and Unicode

Total characters84821
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowCalifornia
4th rowFlorida
5th rowFlorida

Common Values

ValueCountFrequency (%)
California 2001
20.0%
New York 1128
 
11.3%
Texas 985
 
9.9%
Pennsylvania 587
 
5.9%
Washington 506
 
5.1%
Illinois 492
 
4.9%
Ohio 469
 
4.7%
Florida 383
 
3.8%
Michigan 255
 
2.6%
North Carolina 249
 
2.5%
Other values (39) 2939
29.4%

Length

2025-07-02T21:55:30.364387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california 2001
17.1%
new 1322
 
11.3%
york 1128
 
9.6%
texas 985
 
8.4%
pennsylvania 587
 
5.0%
washington 506
 
4.3%
illinois 492
 
4.2%
ohio 469
 
4.0%
florida 383
 
3.3%
carolina 291
 
2.5%
Other values (43) 3542
30.3%

Most occurring characters

ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71413
84.2%
Uppercase Letter 11696
 
13.8%
Space Separator 1712
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10758
15.1%
i 9895
13.9%
n 8090
11.3%
o 7323
10.3%
r 5544
7.8%
e 5051
7.1%
l 4822
6.8%
s 4604
6.4%
f 2011
 
2.8%
h 1898
 
2.7%
Other values (14) 11417
16.0%
Uppercase Letter
ValueCountFrequency (%)
C 2566
21.9%
N 1655
14.2%
T 1168
10.0%
Y 1128
9.6%
M 763
 
6.5%
I 748
 
6.4%
O 659
 
5.6%
W 621
 
5.3%
P 587
 
5.0%
F 383
 
3.3%
Other values (11) 1418
12.1%
Space Separator
ValueCountFrequency (%)
1712
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83109
98.0%
Common 1712
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10758
12.9%
i 9895
11.9%
n 8090
 
9.7%
o 7323
 
8.8%
r 5544
 
6.7%
e 5051
 
6.1%
l 4822
 
5.8%
s 4604
 
5.5%
C 2566
 
3.1%
f 2011
 
2.4%
Other values (35) 22445
27.0%
Common
ValueCountFrequency (%)
1712
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10758
12.7%
i 9895
11.7%
n 8090
 
9.5%
o 7323
 
8.6%
r 5544
 
6.5%
e 5051
 
6.0%
l 4822
 
5.7%
s 4604
 
5.4%
C 2566
 
3.0%
f 2011
 
2.4%
Other values (36) 24157
28.5%

Sub-Category
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size626.6 KiB
Binders
1523 
Paper
1370 
Furnishings
957 
Phones
889 
Storage
846 
Other values (12)
4409 

Length

Max length11
Median length9
Mean length7.191715
Min length3

Characters and Unicode

Total characters71874
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowLabels
4th rowTables
5th rowStorage

Common Values

ValueCountFrequency (%)
Binders 1523
15.2%
Paper 1370
13.7%
Furnishings 957
9.6%
Phones 889
8.9%
Storage 846
8.5%
Art 796
8.0%
Accessories 775
7.8%
Chairs 617
6.2%
Appliances 466
 
4.7%
Labels 364
 
3.6%
Other values (7) 1391
13.9%

Length

2025-07-02T21:55:30.479209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
binders 1523
15.2%
paper 1370
13.7%
furnishings 957
9.6%
phones 889
8.9%
storage 846
8.5%
art 796
8.0%
accessories 775
7.8%
chairs 617
6.2%
appliances 466
 
4.7%
labels 364
 
3.6%
Other values (7) 1391
13.9%

Most occurring characters

ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 61880
86.1%
Uppercase Letter 9994
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 9934
16.1%
e 8870
14.3%
r 7169
11.6%
i 5668
9.2%
n 5378
8.7%
a 4542
7.3%
o 3288
 
5.3%
p 3004
 
4.9%
h 2578
 
4.2%
c 2359
 
3.8%
Other values (8) 9090
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 2259
22.6%
A 2037
20.4%
B 1751
17.5%
F 1174
11.7%
S 1036
10.4%
C 685
 
6.9%
L 364
 
3.6%
T 319
 
3.2%
E 254
 
2.5%
M 115
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 71874
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 9934
13.8%
e 8870
12.3%
r 7169
 
10.0%
i 5668
 
7.9%
n 5378
 
7.5%
a 4542
 
6.3%
o 3288
 
4.6%
p 3004
 
4.2%
h 2578
 
3.6%
c 2359
 
3.3%
Other values (18) 19084
26.6%

Year
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size595.5 KiB
2017
3312 
2016
2587 
2015
2102 
2014
1993 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters39976
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2017 3312
33.1%
2016 2587
25.9%
2015 2102
21.0%
2014 1993
19.9%

Length

2025-07-02T21:55:30.576169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-02T21:55:30.654130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 3312
33.1%
2016 2587
25.9%
2015 2102
21.0%
2014 1993
19.9%

Most occurring characters

ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3312
 
8.3%
6 2587
 
6.5%
5 2102
 
5.3%
4 1993
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39976
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3312
 
8.3%
6 2587
 
6.5%
5 2102
 
5.3%
4 1993
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39976
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3312
 
8.3%
6 2587
 
6.5%
5 2102
 
5.3%
4 1993
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9994
25.0%
0 9994
25.0%
1 9994
25.0%
7 3312
 
8.3%
6 2587
 
6.5%
5 2102
 
5.3%
4 1993
 
5.0%

Month
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8096858
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2025-07-02T21:55:30.761746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2846544
Coefficient of variation (CV)0.42058727
Kurtosis-0.99132786
Mean7.8096858
Median Absolute Deviation (MAD)2
Skewness-0.42969297
Sum78050
Variance10.788955
MonotonicityNot monotonic
2025-07-02T21:55:30.852689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
11 1471
14.7%
12 1408
14.1%
9 1383
13.8%
10 819
8.2%
5 735
7.4%
6 717
7.2%
7 710
7.1%
8 706
7.1%
3 696
7.0%
4 668
6.7%
Other values (2) 681
6.8%
ValueCountFrequency (%)
1 381
 
3.8%
2 300
 
3.0%
3 696
7.0%
4 668
6.7%
5 735
7.4%
6 717
7.2%
7 710
7.1%
8 706
7.1%
9 1383
13.8%
10 819
8.2%
ValueCountFrequency (%)
12 1408
14.1%
11 1471
14.7%
10 819
8.2%
9 1383
13.8%
8 706
7.1%
7 710
7.1%
6 717
7.2%
5 735
7.4%
4 668
6.7%
3 696
7.0%

Interactions

2025-07-02T21:55:23.229724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:16.690280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.486133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.466244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.468415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:20.729305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.881049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.346338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:16.826509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.795364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.584836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.655424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:20.880031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.065829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.482573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:16.937268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.898414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.716995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.816885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.025990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.229329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.586790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.053378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.007059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.823924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.981710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.180422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.402302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.695000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.167539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.136796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.985301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:20.163821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.390509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.579596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.801644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.276119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.242126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.152806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:20.362796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.535927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.751283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:23.912497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:17.388682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:18.359354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:19.308844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:20.571914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:21.722844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-02T21:55:22.873117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-02T21:55:30.964489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CategoryDays to Ship ActualDays to Ship ScheduledDiscountMonthProfit RatioProfit per OrderQuantityRegionSales per CustomerSegmentShip ModeShip StatusStateSub-CategoryYear
Category1.0000.0000.0000.3770.0140.2710.0560.0000.0000.0720.0000.0000.0000.0190.9990.000
Days to Ship Actual0.0001.0000.781-0.0140.0040.004-0.0070.0160.041-0.0150.0430.6660.7800.1020.0010.047
Days to Ship Scheduled0.0000.7811.0000.0270.0440.0120.0050.0000.0220.0000.0331.0000.5060.0960.0070.023
Discount0.377-0.0140.0271.000-0.001-0.645-0.543-0.0010.294-0.0570.0050.0370.0210.3540.3530.000
Month0.0140.0040.044-0.0011.0000.0020.0170.0220.0400.0150.0420.0310.0520.0980.0000.026
Profit Ratio0.2710.0040.012-0.6450.0021.0000.5000.0000.203-0.2010.0170.0200.0190.2280.3040.000
Profit per Order0.056-0.0070.005-0.5430.0170.5001.0000.2340.0210.5180.0000.0000.0120.0170.1300.000
Quantity0.0000.0160.000-0.0010.0220.0000.2341.0000.0000.3270.0120.0000.0090.0040.0000.013
Region0.0000.0410.0220.2940.0400.2030.0210.0001.0000.0000.0000.0300.0070.9980.0000.016
Sales per Customer0.072-0.0150.000-0.0570.015-0.2010.5180.3270.0001.0000.0020.0000.0000.0000.1420.000
Segment0.0000.0430.0330.0050.0420.0170.0000.0120.0000.0021.0000.0070.0270.0900.0000.028
Ship Mode0.0000.6661.0000.0370.0310.0200.0000.0000.0300.0000.0071.0000.4220.1040.0170.026
Ship Status0.0000.7800.5060.0210.0520.0190.0120.0090.0070.0000.0270.4221.0000.0920.0000.048
State0.0190.1020.0960.3540.0980.2280.0170.0040.9980.0000.0900.1040.0921.0000.0000.089
Sub-Category0.9990.0010.0070.3530.0000.3040.1300.0000.0000.1420.0000.0170.0000.0001.0000.000
Year0.0000.0470.0230.0000.0260.0000.0000.0130.0160.0000.0280.0260.0480.0890.0001.000

Missing values

2025-07-02T21:55:24.099454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-02T21:55:24.317479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Days to Ship ActualShip StatusDays to Ship ScheduledSales per CustomerProfit RatioCategoryCityCountryCustomer NameDiscountOrder DateProduct NameQuantityRegionProfit per OrderSegmentShip ModeStateSub-CategoryYearMonth
0313261.9616.0FurnitureHendersonUnited StatesClaire Gute0.02016-11-08Bush Somerset Collection Bookcase2South41.91Consumer1.0KentuckyBookcases201611
1313731.9430.0FurnitureHendersonUnited StatesClaire Gute0.02016-11-08Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back3South219.58Consumer1.0KentuckyChairs201611
24-1314.6247.0Office SuppliesLos AngelesUnited StatesDarrin Van Huff0.02016-06-12Self-Adhesive Address Labels for Typewriters by Universal2West6.87Corporate1.0CaliforniaLabels20166
37-16957.58-40.0FurnitureFort LauderdaleUnited StatesSean O'Donnell45.02015-10-11Bretford CR4500 Series Slim Rectangular Table5South-383.03Consumer0.0FloridaTables201510
47-1622.3711.3Office SuppliesFort LauderdaleUnited StatesSean O'Donnell20.02015-10-11Eldon Fold 'N Roll Cart System2South2.52Consumer0.0FloridaStorage201510
552648.8629.0FurnitureLos AngelesUnited StatesBrosina Hoffman0.02014-06-09Eldon Expressions Wood and Plastic Desk Accessories, Cherry Wood7West14.17Consumer0.0CaliforniaFurnishings20146
65267.2827.0Office SuppliesLos AngelesUnited StatesBrosina Hoffman0.02014-06-09Newell 3224West1.97Consumer0.0CaliforniaArt20146
7526907.1510.0TechnologyLos AngelesUnited StatesBrosina Hoffman20.02014-06-09Mitel 5320 IP Phone VoIP phone6West90.72Consumer0.0CaliforniaPhones20146
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